Data driven soft sensor development for complex chemical processes using extreme learning machine

He, Yan‐Lin; Geng, Zhiqiang; Zhu, Qunxiong

doi:10.1016/j.cherd.2015.06.009

Cited by 75 publications

(12 citation statements)

References 41 publications

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“…There are many examples of the use of these machine learning techniques for modeling soft-sensors (e.g., [20,21]). Specifically, these techniques have been used successfully in WWTPs, as shown below.…”

Section: Materials and Methodsmentioning

confidence: 99%

Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Hernández-del-Olmo

Gaudioso

Duro

et al. 2019

Sensors

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Control of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.

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Section: Materials and Methodsmentioning

confidence: 99%

Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Hernández-del-Olmo

Gaudioso

Duro

et al. 2019

Sensors

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“…Common methods of correlation analysis, such as a principal component analysis (Wold, Esbensen, & Geladi, 1987), a canonical correlation analysis (Hardoon, Szedmak, & Shawe-Taylor, 2004), and a Pearson analysis (He, Geng, & Zhu, 2015), require that the data have a Gaussian distribution. However, except for T BRP , the data for a sintering process do not have such a distribution.…”

Section: Correlation Analyses Of Process Datamentioning

confidence: 99%

Modeling and optimization method featuring multiple operating modes for improving carbon efficiency of iron ore sintering process

Chen

et al. 2016

Control Engineering Practice

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“…In recent years, ELM has gained growing popularity in soft sensor applications, due to its fast learning speed and good generalization performance [63][64][65][66]. However, ELM often produces unstable predictions, due to the uncertainties caused by the random assignments of input weights and biases in the learning process.…”

Section: Nclelmmentioning

confidence: 99%

Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

You-wei

Jin

Dong

et al. 2021

Sensors

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Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes. However, labeled data are often scarce in many real-world applications, which poses a significant challenge when building accurate soft sensor models. Therefore, this paper proposes a novel semi-supervised soft sensor method, referred to as ensemble semi-supervised negative correlation learning extreme learning machine (EnSSNCLELM), for industrial processes with limited labeled data. First, an improved supervised regression algorithm called NCLELM is developed, by integrating the philosophy of negative correlation learning into extreme learning machine (ELM). Then, with NCLELM as the base learning technique, a multi-learner pseudo-labeling optimization approach is proposed, by converting the estimation of pseudo labels as an explicit optimization problem, in order to obtain high-confidence pseudo-labeled data. Furthermore, a set of diverse semi-supervised NCLELM models (SSNCLELM) are developed from different enlarged labeled sets, which are obtained by combining the labeled and pseudo-labeled training data. Finally, those SSNCLELM models whose prediction accuracies were not worse than their supervised counterparts were combined using a stacking strategy. The proposed method can not only exploit both labeled and unlabeled data, but also combine the merits of semi-supervised and ensemble learning paradigms, thereby providing superior predictions over traditional supervised and semi-supervised soft sensor methods. The effectiveness and superiority of the proposed method were demonstrated through two chemical applications.

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Data driven soft sensor development for complex chemical processes using extreme learning machine

Cited by 75 publications

References 41 publications

Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Modeling and optimization method featuring multiple operating modes for improving carbon efficiency of iron ore sintering process

Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

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